Artefact Detection

Artefact detection focuses on identifying and removing unwanted distortions or errors in various data types, including images (medical, astronomical, photographic) and video streams, to improve data quality and downstream analysis. Current research emphasizes the development and application of machine learning models, particularly convolutional neural networks and transformers, often leveraging techniques like transfer learning and semi-supervised learning to address data scarcity and annotation challenges. These advancements are crucial for enhancing the reliability of scientific analyses across diverse fields, from medical diagnosis to astronomical observation, and improving the user experience in applications like video streaming.